CN113096404B - Road blockade oriented quantitative calculation method for change of traffic flow of road network - Google Patents

Road blockade oriented quantitative calculation method for change of traffic flow of road network Download PDF

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CN113096404B
CN113096404B CN202110442022.3A CN202110442022A CN113096404B CN 113096404 B CN113096404 B CN 113096404B CN 202110442022 A CN202110442022 A CN 202110442022A CN 113096404 B CN113096404 B CN 113096404B
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road section
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traffic flow
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CN113096404A (en
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刘宝举
邓敏
龙军
石岩
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Central South University
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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/065—Traffic control systems for road vehicles by counting the vehicles in a section of the road or in a parking area, i.e. comparing incoming count with outgoing count

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Abstract

The invention discloses a road block oriented quantitative calculation method for road network traffic flow change, which comprises the steps of firstly providing a quantitative expression model of road section node flow dynamic values by analyzing the characteristics of road network flow time dependence and flow accumulation, providing a cascade reaction function of flow change rate between the origin and destination road section nodes by tracking the tiny disturbance diffusion propagation process of the road network flow, further solving a flow disturbance response matrix between road sections, and finally providing a correlation model integrating a time lag operator and a space diffusion operator to calculate the road network traffic flow change caused by traffic flow time-space delay on the road section correlation. The method tracks and captures response steady-state values of other road sections by loading trace traffic flow disturbance on any road section node, can detect the dynamic time-space correlation of the space local road section node, and accurately calculates the traffic flow change of a road network.

Description

Road blockade oriented quantitative calculation method for change of traffic flow of road network
Technical Field
The invention belongs to the field of traffic engineering, and particularly relates to a road network traffic flow change quantitative calculation method for road blockade.
Background
Nowadays, vehicles on roads are increased day by day, and the problem of road congestion is more and more serious, so that the traffic state of a road network needs to be predicted.
Assuming that some sudden events of capacity attenuation or failure of local nodes and road sections occur in the road network, various traffic flow indexes after the road sections fail are subjected to statistical analysis through a traffic flow distribution means, so that the importance and the relevance of each road section node in the road network are determined. The innovation points of the method are focused on three aspects of a road section failure strategy, a traffic flow distribution method and a road section evaluation index. In terms of a road segment failure strategy, Liu et al firstly proposes a road segment attack strategy with a priority on node degree and considers that the traffic capacity of a road network can be improved under a specific condition (Liu, et al, 2007). Thereafter, Zhang et al compared two different attack strategies, node degree-first and betweenness-first, and indicated the difference in the influence of the two on the road network transmission (Zhang, et al, 2007). Huang considers that the two attack strategies cannot effectively optimize the global transport capacity of the road network, so a new road section deletion strategy is provided based on a simulated annealing algorithm, and the balanced distribution of network flow of the height nodes and the neighborhood nodes can be ensured (Huang, et al, 2010). In addition, according to different urban road network evaluation targets, relevant scholars design various road section failure strategies (Wisetjindawat, et al., 2015; Jenlius, et al.,2015) such as cascade failure, random edge reconnection, edge reconnection based on a high-order value and edge reconnection based on a high-order value to influence the transmission performance of the road network by various broken edge reconnection strategies (Jiang, et al., 2014).
The static topological structure and the dynamic traffic flow characteristics of the urban road network are usually difficult to be fused and analyzed, so that the prior evaluation method mostly focuses on the global evaluation index of the static road network and ignores the dynamic change process of the static road network, and lacks of discussing the dynamic influence of the traffic flow propagation effect on the road network structure and the local correlation of the road space structure, thereby inaccurate calculation of the change of the road network traffic flow.
Disclosure of Invention
The invention aims to provide a road block-oriented road network traffic flow change quantitative calculation method, which is used for calculating dynamic disturbance of a traffic flow to a road section by constructing a traffic flow dynamic transmission process description model, and provides a traffic flow change calculation method based on a traffic flow dynamic model, which is more accurate in calculation and can consider more dynamic variables.
The invention provides a road network traffic flow change quantitative calculation method for road blockade, which comprises the following steps:
s1, a quantitative expression model of a road section node flow dynamic value is provided by analyzing the characteristics of time dependence and flow accumulation of the road network flow;
s2, providing a cascade reaction function of flow rate change between nodes of the origin-destination road section by tracking a micro disturbance diffusion propagation process of the flow of the road network;
s3, providing a traffic flow disturbance response matrix between road sections;
s4, providing a correlation model integrating a time lag operator and a space diffusion operator to evaluate the influence of traffic flow time-space delay on road section correlation;
s5, providing road section dynamic association degree measurement, and calculating the change of road network traffic flow when a road is blocked;
the step S1 is specifically to provide a dynamic traffic system, calculate the route node rjFlow disturbance of
Figure GDA0003408220090000021
Cause time T node riInstantaneous value of flow of
Figure GDA0003408220090000022
Comprises the following steps:
Figure GDA0003408220090000023
wherein x isi(t0) As a node r of a road sectioniAt t0A flow value at a time; t is t 00 denotes a node riThe initial flow rate of (a);
Figure GDA0003408220090000024
to a node r of the road section at time tjFlow disturbance of
Figure GDA0003408220090000025
To road section node riIs used for representing the road section node r at the time tiInstantaneous traffic variation.
The instantaneous traffic variation is determined according to a general equation of a network dynamic process, specifically, a weighted directed network A is arrangedijIncluding N nodes, each node having its attribute value x time-dependenti(t), the network dynamics are expressed as the following general equation:
Figure GDA0003408220090000026
wherein the content of the first and second substances,
Figure GDA0003408220090000027
representing road section nodes riThe derivative of the flow value of (a) at time t; x is the number ofi(t) is a link node riA flow value at time t; x is the number ofj(t) is a link node rjA flow value at time t; m0(xi(t)) represents a link node xiAutomatic state change of (2);
Figure GDA0003408220090000028
representing a captured road segment node rjTo road section node riCascading effects of attribute values; m ═ M (M)0(x),M1(x),M2(x) ) is a nonlinear system of equations describing the dynamic process of a complex network system; m1(xi(t)) is a link node riIncrement of flow rate value of delta (x)i(t))；M2(xj(t)) is a link node rjInverse 1/delta (x) of flow value incrementj(t))。
The step S2 is specifically to set the existing traffic flow to pass through the link nodes r in sequence from the O-linkjRoad section node rkAnd road section node riReach D road section, and at the same time, reach the node r of the road sectionjConnecting road section nodes r through first blank nodesiRoad section node rkConnecting the D road section through a second blank node; road section node rjSubject to a disturbance of flow of
Figure GDA0003408220090000029
Road section node r after time tiFor traffic flow
Figure GDA00034082200900000210
Represents; according to the general equation of network dynamic process
Figure GDA00034082200900000211
Is interfered by two parts of flow, including traffic outflow and traffic inflow;
the traffic outflow is from the node r of the road section within the time from 0 to tiOutgoing traffic volume; the traffic inflow is defined by a road section node r in the time of 0-tjFlow direction road section node riThe amount of traffic of (2); road section node rjAnd road section node riA plurality of flow channels exist between the two channels, and the traffic flow is only influenced by the upstream; road section node riTraffic flow of
Figure GDA00034082200900000212
Comprises the following steps:
Figure GDA00034082200900000213
wherein, within the time from 0 to t,
Figure GDA00034082200900000214
as a node r of a road sectionjDisturbed flow
Figure GDA00034082200900000215
In the case of (2), link node riTraffic outflow variation of (1);
Figure GDA0003408220090000031
as a node r of a road sectionkTo road section node rjTraffic inflow variation amount of (a); n is a radical ofiAs a node r of a road sectioniThe number of upstream adjacent road segment nodes.
The step S3 is specifically that the traffic flow disturbance response matrix GijIs defined as follows:
Figure GDA0003408220090000032
wherein T is time; n is a radical ofijTo slave link node rjTo road section node riRoad segment node r included in the feasible path of (1)iThe number of upstream contiguous segments of (a); k represents a link node rkID identification of (a); rik(T) is a road section riTo road section node r at time TkLocal response to traffic flow disturbances; gkj(T) is a link node rkTo road section node r at time TjResponse to traffic flow disturbances.
Traffic flow disturbance response matrix GijThe calculation method is specifically that the road section rkAt time T to segment node rjLocal response R of traffic flow disturbancekj(T) is expressed as:
Figure GDA0003408220090000033
wherein T is time; x is the number ofk(T) is a link node rkA traffic flow response value at time T; x is the number ofj(T) is a link node rjA traffic flow response value at time T;
traffic flow disturbance response matrix GijBy tracking the source road section node riLocal disturbance to the traffic flow at a certain time affects the traffic flow activity of all the nodes of the remaining road segments in the traffic system to track the propagation of traffic signals in the dynamic traffic system proposed in step S1, thereby generating a traffic flow response matrix; and according to the link node r in the step S2iTraffic flow of
Figure GDA0003408220090000034
Figure GDA0003408220090000035
Road section node riAt time T to segment node rjResponse expression G of traffic flow disturbanceij(T) is:
Figure GDA0003408220090000036
wherein dx isj(t) is a link node rjA traffic change at time t; k represents a link node rkID identification of (a); n is a radical ofijTo slave link node rjTo road section node riRoad segment node r included in the feasible path of (1)iThe number of upstream contiguous segments of (a);
Figure GDA0003408220090000037
as a node r of a road sectionjDisturbed flow
Figure GDA0003408220090000038
In the case of (2), link node riTraffic outflow variation of (1);
Figure GDA0003408220090000039
as a node r of a road sectionkTo road section node rjTraffic inflow variation amount of (a); x is the number ofi(T) is a link node riA traffic flow response value at time T; x is the number ofj(T) is a link node rjA traffic flow response value at time T;
Figure GDA00034082200900000310
representing an automatic flow rate of change;
Figure GDA00034082200900000311
representing a road section rjFor road section riThe effect of the rate of change of flow;
the diffusion propagation of the flow in the road network from the O road section to the D road section can generate the cascade effect of the flow attribute value rjThe flow of the node firstly influences the local neighborhood road section rkFlow rate value of (2), further to riThe road section is propagated, therefore, rjTo riIs converted into rj→rk→riChain reaction, in particular connecting road section nodes riAt time T to segment node rjResponse expression G of traffic flow disturbanceij(T) is expressed as:
Figure GDA0003408220090000041
wherein dx isj(T) is a time T road sectionNode rjThe difference between the steady state attribute value of (a) and the initial value; dx (x)k(T) is a link node rkA traffic change at time T; n is a radical ofijTo slave link node rjTo road section node riRoad segment node r included in the feasible path of (1)iThe number of upstream contiguous segments of (a); x is the number ofi(T) is a link node riA traffic flow response value at time T; x is the number ofj(T) is a link node rjA traffic flow response value at time T; j represents a link node rjID identification of (a);
Figure GDA0003408220090000042
is the automatic flow rate of change;
Figure GDA0003408220090000043
is the cascade effect of the road section flow rate change.
The step S4 is specifically to set a time lag operator as S; x and y are traffic flow response matrix G of two road sectionsijSample, then the correlation ρ(s) between the road segments x and y at time delay s can be expressed as:
Figure GDA0003408220090000044
wherein, muxThe mean value of the traffic flow of the road section node x is shown; mu.syThe average value of the traffic flow of the road section node y is shown; cov (,) is covariance; e (-) is the mathematical expectation;
Figure GDA0003408220090000045
is the variance of the traffic flow of the road segment node x;
Figure GDA0003408220090000046
the variance of the traffic flow is taken as a road section node y; y (t + s) is a traffic flow response value of the y road section at the moment of t + s; x (t) is the traffic flow response value of the x road section at the time t;
however, road segment nodes in an urban traffic network are typically associated with multiple neighborhood road segments, namelyThe spatial correlation range of the local correlation of the characteristic road section is described, and the weighted average response value of the k-order neighborhood road section is described by a spatial diffusion operator k
Figure GDA0003408220090000047
Figure GDA0003408220090000048
Wherein the content of the first and second substances,
Figure GDA0003408220090000049
representing road section nodes raFlow response generated by disturbance flows of all road sections in a road network; n is the number of road sections in the road network; omegaijAs a node r of a road sectioniAnd road section node rjThe relation weight of (a) is determined according to the neighborhood parameter k; mk is the number of k-order neighborhood road sections adjacent to the road section ri in the road network; b is a road section node rbID identification of (a); j is a road section node rjID identification of (a);
the influence of the time lag operator s and the spatial diffusion operator k on the local correlation of the road network is integrated, and the local space-time heterogeneity rho of the traffic flow on the road section is reflected by describing the incidence relation of traffic flow response between the road section and the k adjacent road sections in the traffic network with time delayi(k, s), specifically expressed as:
Figure GDA0003408220090000051
Figure GDA0003408220090000052
average value of the response values;
Figure GDA0003408220090000053
as a node r of a road sectioniThe k-order neighborhood road section traffic flow response value of the road section is obtained.
The step S5 is specifically to respond to the traffic flow disturbance moment in the step S3Array GijRoad network global traffic flow to road section node riDynamic disturbance response R ofi(T) quantified as:
Figure GDA0003408220090000054
wherein j is not equal to i; j represents a link node rjID identification of (a); i represents a link node riID identification of (a); n is the number of all nodes in the road network; gij(T) is a link node riAt time T to segment node rjResponse expression of traffic flow disturbance;
introducing a link variation set parameter pi ═ pia,πbIn which, pia＝{(rl,ρl) 1, 2.,. na } represents a link failure set, where r islNumbering failed road sections, plReduction ratio rho for road transport capacityl∈[0,1]And na is the number of failed road sections; pib＝{(qf,of,df) 1,2, 9, nb is a newly added road section set, qfNumbering newly added road sections; ofIs the starting point ID of the initial path; dfThe number of the newly added road sections is nb;
road section riThe degree of dynamic association with other road segments, Cor (pi, i), is:
Figure GDA0003408220090000055
wherein j is not equal to i; j represents a link node rjID identification of (a); i represents a link node riID identification of (a); ri(T) is a road section riResponding to dynamic disturbance of the road network global traffic flow;
Figure GDA0003408220090000056
for node r of global traffic flow of road network after introducing variable set parameters of road sectionsiThe dynamic disturbance response of (2); n is the number of all nodes in the road network; gij(T) is a link node riAt time T to segment node rjResponse expression of traffic flow disturbance;
Figure GDA0003408220090000057
and (5) traffic flow disturbance response matrixes after the pi change and flow redistribution are carried out on the road section set.
The road network traffic flow change quantitative calculation method for road blockade provided by the invention tracks and captures response steady-state values of other road sections by loading trace traffic flow disturbance on any road section node, can detect the dynamic time-space correlation of the space local road section node, and accurately calculates the traffic flow change of a road network.
Drawings
FIG. 1 is a schematic flow diagram of the process of the present invention.
FIG. 2 is a traffic flow disturbance propagation process diagram of the method of the present invention.
Fig. 3 is a schematic diagram of the local correlation of the road sections according to the method of the invention.
Fig. 4 is a schematic diagram of a link variation of the method of the present invention.
FIG. 5 is a schematic view of traffic flow before and after a link change according to the method of the present invention.
Fig. 6 is a schematic diagram of simulated network verification data according to an embodiment of the present invention.
Fig. 7 is a schematic diagram of a traffic flow disturbance dynamic response result according to an embodiment of the present invention.
FIG. 8 is a schematic diagram of a representative road section traffic flow disturbance dynamic response according to an embodiment of the invention
Fig. 9 is a schematic diagram of local spatiotemporal correlation of an Nguyen network according to an embodiment of the present invention.
FIG. 10 is a first diagram of the spatial-spatial temporal correlation of the real road network according to an embodiment of the present invention.
Fig. 11 is a second schematic diagram of the spatial-spatial correlation of the real road network according to the embodiment of the present invention.
FIG. 12 is a third schematic diagram of the spatial-local spatiotemporal correlation of the real road network according to the embodiment of the present invention.
Fig. 13 is a schematic diagram of a relationship between a representative link correlation and a time delay when k is 1 according to an embodiment of the present invention.
Fig. 14 is a schematic diagram of a spatial visualization of local spatiotemporal correlation of an urban road network according to an embodiment of the present invention.
Fig. 15 is a schematic diagram of a spatial remote correlation association mode of a typical road segment according to an embodiment of the present invention.
Detailed Description
FIG. 1 is a schematic flow chart of the method of the present invention: the invention provides a road network traffic flow change quantitative calculation method for road blockade, which comprises the following steps:
s1, a quantitative expression model of a road section node flow dynamic value is provided by analyzing the characteristics of time dependence and flow accumulation of the road network flow;
s2, providing a cascade reaction function of flow rate change between nodes of the origin-destination road section by tracking a micro disturbance diffusion propagation process of the flow of the road network;
s3, providing a traffic flow disturbance response matrix between road sections;
s4, providing a correlation model integrating a time lag operator and a space diffusion operator to evaluate the influence of traffic flow time-space delay on road section correlation;
and S5, providing road section dynamic association degree measurement, and calculating the change of road network traffic flow when the road is blocked.
Step S1 is that the dynamic change process of the road section node flow value can be visually expressed by developing the time dimension, the road network flow has typical time delay and flow instability, and the transport time and the initial flow have no definite linear correlation with the flow steady-state value of the road section node; the node flow values exhibit a delay cumulative effect in the time dimension. Therefore, a dynamic traffic system is proposed, calculating the route node rjFlow disturbance of
Figure GDA0003408220090000061
Cause time T node riThe instantaneous flow values are:
Figure GDA0003408220090000062
wherein x isi(t0) As a node r of a road sectioniAt t0A flow value at a time; t is t 00 denotes a link node riThe initial flow rate of (a);
Figure GDA0003408220090000063
to a node r of the road section at time tjFlow disturbance of
Figure GDA0003408220090000064
To road section node riIs used for representing the road section node r at the time tiInstantaneous traffic variation.
The instantaneous traffic variation is determined according to a general equation of a network dynamic process, and a network dynamic system has led to extensive research in the fields of ecological species interaction, epidemic disease propagation, protein dynamic modeling and the like, in particular to a weighted directed network AijIncluding N road section nodes, the attribute value of each road section node being x dependent on timei(t), the network dynamics are expressed as the following general equation:
Figure GDA0003408220090000071
wherein the content of the first and second substances,
Figure GDA0003408220090000072
representing road section nodes riThe derivative of the flow value of (a) at time t; x is the number ofi(t) is a link node riA flow value at time t; x is the number ofj(t) is a link node rjA flow value at time t; m0(xi(t)) represents a link node xiAutomatic state change of (2);
Figure GDA0003408220090000073
catching road section node rjTo road section node riCascading effects of attribute values; m ═ M (M)0(x),M1(x),M2(x) ) is a non-linear system of equations describing a complex netDynamic processes of the network system; in the examples of the present invention M1(xi(t)) is a link node riIncrement of flow rate value of delta (x)i(t))；M2(xj(t)) is a link node rjInverse 1/delta (x) of flow value incrementj(t))。
Step S2 is, specifically, as shown in fig. 2, a traffic flow disturbance propagation process diagram of the method of the present invention; it is assumed that the existing traffic flow passes through the road section node r from the O road section (starting point) in turnjRoad section node rkAnd road section node riReach D link (terminal) and at the same time, link node rjConnecting road section nodes r through first blank nodesiRoad section node rkConnecting the D road section through a second blank node; road section node rjSubject to a disturbance of flow of
Figure GDA0003408220090000074
Road section node r after time tiFor traffic flow
Figure GDA0003408220090000075
Represents; according to the general equation of network dynamic process and due to traffic flow
Figure GDA0003408220090000076
Is subject to two-part flow disturbances, including traffic outflow (r)i→ D) and traffic inflow (r)j→ri)；
The traffic outflow is from the node r of the road section within the time from 0 to tiOutgoing traffic volume; the traffic inflow is defined by a road section node r in the time of 0-tjFlow direction road section node riThe amount of traffic of (2); road section node rjAnd road section node riA plurality of flow channels exist between the two channels, and the traffic flow is only influenced by the upstream;
thus, the link node riTraffic flow of
Figure GDA0003408220090000077
Comprises the following steps:
Figure GDA0003408220090000078
wherein, within the time from 0 to t,
Figure GDA0003408220090000079
as a node r of a road sectionjDisturbed flow
Figure GDA00034082200900000710
In the case of (2), link node riTraffic outflow variation of (1);
Figure GDA00034082200900000711
as a node r of a road sectionkTo road section node riTraffic inflow variation amount of (a); n is a radical ofiAs a node r of a road sectioniThe number of upstream adjacent road segment nodes.
Step S3 is embodied as RkjTraceable slave road section rjTo road section rkThe influence of traffic disturbance in the unique direction of the network is independent of the interference of other nodes in the network. Traffic flow disturbance response matrix GijIs defined as follows:
Figure GDA00034082200900000712
wherein T is time; n is a radical ofijTo slave link node rjTo road section node riRoad segment node r included in the feasible path of (1)iThe number of upstream contiguous segments of (a); k represents a link node rkID identification of (a); rik(T) is a road section riTo road section node r at time TkLocal response to traffic flow disturbances; gkj(T) is a link node rkAt time T to segment node rjResponse to traffic flow disturbances.
Set road section rkAt time T to segment node rjThe local response of traffic flow disturbances is expressed as:
Figure GDA0003408220090000081
wherein x isk(T) is a link node rkA traffic flow response value at time T; x is the number ofj(T) is a link node rjA traffic flow response value at time T;
by tracing the source road section node riHow the local disturbance to the traffic flow at a certain moment affects the traffic flow activities of all the nodes of the rest road sections in the traffic system to track the propagation of traffic signals in the dynamic traffic system proposed in the step S1, so as to generate a traffic flow response matrix, and replace static indexes such as network topology and the like as the basis of dynamic evaluation of the traffic network; and according to the link node r in the step S2iTraffic flow of
Figure GDA0003408220090000082
Road section riAt time T to segment node rjThe response of traffic flow disturbances is expressed as:
Figure GDA0003408220090000083
wherein dx isj(t) is a link node rjA traffic change at time t;
Figure GDA0003408220090000084
as a node r of a road sectionjDisturbed flow
Figure GDA0003408220090000085
In the case of (2), link node riTraffic outflow variation of (1);
Figure GDA0003408220090000086
as a node r of a road sectionkTo road section node rjTraffic inflow variation amount of (a); x is the number ofi(T) is a link node riA traffic flow response value at time T; x is the number ofj(T) is a link node rjA traffic flow response value at time T; k represents a link node rkID identification of (a);
Figure GDA0003408220090000087
representing an automatic flow rate of change;
Figure GDA0003408220090000088
representing a road section rjFor road section riThe effect of the rate of change of flow;
as shown in FIG. 2, the diffusion propagation of traffic in the road network from the O-link to the D-link generates the cascade effect of the traffic attribute values, and the link node rjThe flow of (a) will first influence its local neighborhood road section node rkFlow rate value of (2), further to riLink propagation, and therefore link node rjTo road section node riIs converted into rj→rk→riThe chain reaction comprises the following steps:
Figure GDA0003408220090000089
wherein N isijTo slave link node rjTo road section node riRoad segment node r included in the feasible path of (1)iThe number of upstream contiguous segments of (a); dx (x)j(T) is a road section node r at the time of TjThe difference between the steady state attribute value of (a) and the initial value; dx (x)k(T) is a link node rkA traffic change at time T; n is a radical ofijTo slave link node rjTo road section node riRoad segment node r included in the feasible path of (1)iThe number of upstream contiguous segments of (a); x is the number ofi(T) is a link node riA traffic flow response value at time T; x is the number ofj(T) is a link node rjA traffic flow response value at time T; k represents a link node rkID identification of (a); j represents a link node rjID identification of (a);
Figure GDA0003408220090000091
is the automatic flow rate of change;
Figure GDA0003408220090000092
is the cascade effect of the road section flow rate change.
Step S4 is embodied by the traffic flow response matrix GijSimultaneously analyzing two dimensions of time and space, measuring the mobility and the similarity of traffic flow data of a road network so as to detect the time-space local area correlation characteristics of the traffic flow and evaluate the influence of time lag and space diffusion on the road traffic flow correlation:
the urban road network has a specific spatial distribution form, and from the perspective of a network topology structure, the urban road network has strong local correlation in spatial dimension, which is a common consensus in the academic world, however, the time dependence characteristic of traffic flow enables the road network to have significant space-time characteristics. FIG. 3 is a schematic diagram of local relevance of road segments according to the method of the present invention, in which a road network not only has local relevance with surrounding neighborhood road segment nodes, but also road segment nodes r are compared with the neighborhood nodes after traffic flow diffusioniPossibly with road section node rjThere are link-remote correlation modes, and these link nodes with abnormal correlation are usually core links of the regulated traffic network.
The correlation function used by the invention can measure the correlation of different space variables after a fixed time interval. Let the time lag operator be s, x and y be the traffic flow response matrix G of two road sectionsijSample, then the correlation between road segments x and y at time delay s can be expressed as:
Figure GDA0003408220090000093
wherein, muxIs the average of the traffic flows for segment x; mu.syThe average value of the traffic flow of the road section y;
Figure GDA0003408220090000094
is the variance of the traffic flow for road segment x;
Figure GDA0003408220090000095
method for traffic flow of road section yA difference; y (t + s) is a traffic flow response value of the y road section at the moment of t + s; x (t) is the traffic flow response value of the x road section at the time t;
however, road section nodes in urban traffic networks are usually related to a plurality of neighborhood road sections, and in order to characterize the spatial correlation range of road section local correlation, the weighted average response value of k-order neighborhood road sections is described by means of a spatial diffusion operator k
Figure GDA0003408220090000096
Figure GDA0003408220090000097
Wherein the content of the first and second substances,
Figure GDA0003408220090000098
representing a road section raFlow response generated by disturbance flows of all road sections in a road network; n is the number of road sections in the road network; omegaijAs a node r of a road sectioniAnd road section node rjThe relation weight of (a) is determined according to the neighborhood parameter k; mk is the number of k-order neighborhood road sections adjacent to the road section ri in the road network; b is a road section node rbID identification of (a); j is a road section node rjID of (2).
The method integrates the influence of a time lag operator s and a spatial diffusion operator k on the local correlation of the road network, and further reflects the local space-time heterogeneity of the traffic flow on the road section by describing the incidence relation of traffic flow response between the road section and the k adjacent road section in the traffic network with time delay, and is specifically expressed as follows:
Figure GDA0003408220090000101
wherein x isi(t) is a road section riA traffic flow response value at time t;
Figure GDA0003408220090000102
is a section of road riThe sum of the k-order neighborhood road section traffic flow response value at the time of t + sA weight average value;
Figure GDA0003408220090000103
is a section of road riThe average value of the traffic flow response values at the time t;
Figure GDA0003408220090000104
is a section of road riThe k-order neighborhood road section traffic flow response value of the road section is obtained.
If ρi(k, s) is significantly greater than 0, indicating a link node riThe road segment has strong positive correlation with the k-order neighborhood road segment after the traffic flow time delay s; otherwise, if ρi(k, s) approaching 0 indicates a link node riAnd no obvious correlation exists between the k-order neighborhood road section and the k-order neighborhood road section after the traffic flow time delay s, namely the part of road section is not tightly coupled with a traffic network and is usually a key node for regulating smooth operation of the traffic flow.
The step S5 is to determine that the importance of the road network node is not only related to its own attribute and its neighboring node attribute, but also serves as a main bearer system for traffic flow, and the road segment nodes may have a relatively significant relation with the traffic flow as a link and non-neighboring road segment nodes, and the evaluation of the node importance needs to consider the influence of the road segment on the global flow, so the research defines the road segment importance as the contribution of the road segment node to the global traffic flow. The study is based on the principle that random attack of the road section nodes or target node failure is a common means for evaluating the contribution degree of the road section to the flow, and the study is combined with a time-discrete dynamic flow distribution method to redistribute the flow traffic demand after the road section fails so as to dynamically analyze the change process of the relevance and the importance degree of the road section nodes at discrete time.
Traffic flow disturbance response matrix GijQuantifies road section node riAnd road section node rjBut the response between every two road sections does not consider the diffusion influence of complex traffic flow on other road sections, so the invention firstly superposes all traffic flow signals on the road section riInfluence of dynamic disturbance of Ri(T), i.e. Ri(T) is a road section riResponding to dynamic disturbance of the road network global traffic flow; then theBy comparing node r of cut-off road sectioniFront and rear traffic distribution result evaluation road section riAnd associating the strength with the dynamic time sequence of other road sections. According to the traffic flow disturbance response matrix G in the step S3ijGlobal traffic flow of road network to road section riIs quantized to:
Figure GDA0003408220090000105
wherein j is not equal to i; n is the number of all nodes in the road network;
the strategies of random attack, degree-first attack, central priority and the like are road section failure strategies commonly used in the current road section evaluation method, and in the actual traffic control, due to the influence of congestion diffusion or emergency accidents, complex situations that a plurality of road sections fail at the same time, a plurality of temporary road sections are newly increased synchronously and the situations exist alternately can occur; fig. 4 is a schematic diagram of a link variation of the method of the present invention.
The invention introduces a section variation set parameter pi ═ pia,πbIn which, pia＝{(rl,ρl) 1, 2.,. na } represents a link failure set, where r islNumbering failed road sections, plReduction ratio rho for road transport capacityl∈[0,1]And na is the number of failed road sections; pib＝{(qf,of,df) 1,2, 9, nb is a newly added road section set, qfNumbering newly added road sections; ofIs the starting point ID of the initial path; dfThe end point ID of the initial road section and nb are the number of the newly added road sections.
FIG. 5 is a schematic view of the traffic flow before and after the change of the road section according to the method of the present invention; after the road section changes, firstly extracting the OD requirement of the influenced track, taking the k neighborhood of the OD track as a traffic flow influence range, redistributing the traffic flow to the traffic requirement in the range according to the steps S1-S4, and keeping the original tracks of other unaffected areas unchanged so as to improve the flow distribution efficiency; then calculating the traffic flow signal pair road section r at discrete timeiDynamic disturbance influence of
Figure GDA0003408220090000111
By the above formula
Figure GDA0003408220090000112
Thus, the road section riThe dynamic association degree with other road sections is as follows:
Figure GDA0003408220090000113
wherein the content of the first and second substances,
Figure GDA0003408220090000114
collecting a traffic flow disturbance response matrix for the road sections after pi changes and flow redistribution;
detailed description of the invention
To verify the effectiveness of the present invention, as shown in fig. 6, a schematic diagram of simulated network verification data according to an embodiment of the present invention is shown, a classic Nguyen network is used as verification data, so as to facilitate visualization of a dynamic disturbance process of a traffic flow on other road segments and increase interpretability of a result, and 1 starting point and 2 target points are specifically set at an edge of a road network and the same traffic demand is set respectively (200).
By tracking the track of the flow disturbance in the network data, the traffic flow disturbance response G with obvious time-space characteristics can be obtainedijAnd (6) obtaining the result. In terms of spatial characteristics, as shown in fig. 7, which is a traffic flow disturbance dynamic response result diagram in the embodiment of the present invention, since the initial traffic demand is much smaller than the road segment carrying capacity, the optimal path leading to the target point is selected by the traffic flow allocation scheme, two clear tracks are formed between the OD and correspond to the two target points, respectively, and the road segment included in the track generates dynamic response to traffic flow disturbance. From the steady state value after the final road network distribution (T ═ 20), since the traffic demand between the two pairs of ODs 1 → 2 and 1 → 3 is consistent, the traffic flow response values of all the road segments through which the track passes are consistent: g21＝G31＝0.25。
Traffic flow runs different orders in terms of time distribution of traffic flow responseTraffic flow disturbance response matrix G of segmentsijThe difference is obvious, as shown in Table 1, in the initial flowing stage (0) of the traffic flow<T<7) The response of different road sections to traffic disturbance is greatly different (road section 1, road sections 2 and 6, etc.), and the traffic flow disturbance response matrix G of different road sections increases along with the time step lengthijGradually tending to be uniform.
FIG. 8 is a schematic diagram of a traffic disturbance dynamic response of a representative road segment according to an embodiment of the present invention, which is obtained by expanding the traffic disturbance response in a time dimension to find a traffic disturbance response matrix G when no abnormality occurs in the road segment and the traffic flow is normalijCan maintain stability (7)<T<15) When the road section flow is in traffic jam, the traffic flow disturbance response matrix GijA falling wave (14) occurs<T<18) And the initial high response value of the road segment 1 is caused by the initial traffic flow preferentially selecting the road segment 1, the flow gradually diffuses to other road segments ( road segments 2, 4, 6 and 7) along with the increase of time, and the response value of the road segment 1 also gradually restores to the normal level.
TABLE 1 representative road segment traffic flow disturbance dynamic response values
Figure GDA0003408220090000115
Figure GDA0003408220090000121
Traffic flow disturbance response G based on road networkijThe present embodiment further detects the local spatiotemporal correlation of the Nguyen network, and as a result, the present embodiment is shown in fig. 9, and fig. 9 is a schematic diagram of the local spatiotemporal correlation of the Nguyen network according to the embodiment of the present invention. The k represents a k-order neighborhood road section, s is a delay time step length, and rho is correlation, so that the correlation of the road section shows a significant descending trend when the time-space single item delay is increased, and the local road section shows higher time-space correlation when the time-space delay parameter is increased at the same time. This follows the dynamic diffusion law of traffic flow, as shown in table 2, where the flow on a single road segment 6 flows to a 1 st order neighborhood segment (ID ═ 1 ═ when a single time step is delayed7) So the local area network has the highest spatio-temporal correlation when k is 1; the traffic flow is also in a wider neighborhood (links 12,18) as time progresses, so x-order links (x) are delayed by x time<4) The correlation of (a) is generally greater than in the case of k ═ 1, s ═ x or k ═ x, s ═ 1. Furthermore, link nodes have a stronger correlation with their downstream links than with upstream links, because the clear destination directionality of traffic flow and the directionality of links more closely link nodes with downstream links.
TABLE 2 local spatiotemporal correlation of road segments 6
Figure GDA0003408220090000122
The second embodiment is as follows:
the Hankou core business area is a business center, a financial center and a transportation junction in Wuhan city and middle part of China, and smooth traffic is the basic guarantee of the economic stable development of the area. Once traffic jam, accidents and other situations seriously affect the passing of individual road sections, temporary road section blocking, lane blocking and other traffic control measures need to be taken, and how to judge the traffic flow chain reaction caused by road section blocking is a problem of establishing the control measures.
In this embodiment, a wuhan city hankou core area is extracted as an application background, an association mode of a road section in the area is detected, then traffic flow change is calculated, and a traffic control scheme is proposed. FIG. 10 is a first diagram illustrating spatial and temporal correlation of a real road network according to an embodiment of the present invention; FIG. 11 is a second diagram illustrating spatial and temporal correlation of a real road network according to an embodiment of the present invention; fig. 12 is a third schematic diagram of the spatial-local spatiotemporal correlation of the real road network according to the embodiment of the present invention. Most road sections have remarkably strong space-time correlation, and the correlation of the road sections shows a descending trend along with the expansion of space-time delay. Comparing all the road section correlation mean values, when a certain road section in the region is blocked randomly, after 30 seconds (1 time step)Long) will have a strong influence (p) on 1 st order neighborhood segments upstream and downstream thereofmean(up)＝0.80,ρmean(down) ═ 0.86); after 60 seconds (2 time steps), the 2 nd order neighborhood sections upstream and downstream of the link have strong influence (rho)mean(down) ═ 0.84), this effect will be significantly greater than for the 1 st order neighborhood; after 90 seconds (3 time steps) this effect does not propagate significantly to the 3 rd order neighborhood segment upstream and downstream. Therefore, the traffic flow has the characteristics of significant spatial diffusion and time delay, which lead to the correlation on the diagonal lines in fig. 10-12, that is, when blocking a single road segment, the influence of the traffic flow gradually diffuses to a farther road segment along with the increase of time, and the influence on other road segments does not last for a long time.
Further, as shown in table 3, there is a significant difference in the correlation of the link node with its upstream and downstream links, and this difference gradually expands with the increase in time delay. The relevance of the road section and the road section at the downstream is reflected by the liquidity of the traffic flow, namely the traffic flow flows from the current road section to the road section at the downstream; the relevance of the road section and the road section upstream of the road section is reflected by the traffic flow congestion diffusivity, namely, the vehicle queuing state of the current road section influences the road section upstream of the road section, and the influence strength is more rapidly reduced compared with the influence of the downstream liquidity along with the increase of the time delay. In addition, the influence of the increase of the spatial diffusion parameter on the upstream and downstream links is consistent. Therefore, in summary, the longer the time delay, the greater the difference in the correlation between the link node and the link upstream and downstream thereof.
TABLE 3 mean value of real road network spatio-temporal correlation
Figure GDA0003408220090000131
In order to detect the relevance variation characteristics of a single road section, the relevance variation of several road sections which are representative and positioned at different levels of relevance is analyzed. From the perspective of a single link node, as shown in fig. 13, which is a schematic diagram of a relationship between a representative link correlation and a time delay when k is 1 according to an embodiment of the present invention, a local correlation thereof changes smoothly with an increase in the time delay, and a sudden change of the correlation does not occur, that is, an influence on the link block in the area is gradually reduced with an increase in the spatio-temporal distance.
In fig. 10 to 12, there are some fixed links with significant independence from surrounding nodes in the correlation under different space-time delay parameters, and in order to explore the spatial correlation of the nodes of these links, this embodiment projects the result of the local space-time correlation of the road network to the road network space (weighted average of upstream and downstream). Fig. 14 is a schematic diagram illustrating a spatial visualization of local space-time correlation in urban road network according to an embodiment of the present invention, where there is strong correlation between the business circles of roads in south of the east and the south of the research area and the business circles of SOHO in the west of the research area, because these areas are high-occurrence places for various leisure activities such as shopping, entertainment, and playing, the road network connection is more compact, and travelers are more prone to short trip, so blocking the road sections in these areas will obviously affect the adjacent road sections. Meanwhile, road sections with low or even no correlation with surrounding road sections exist in a road network, and through the comparison and analysis of the road structures with the Chinese-character-Kongsan-region road structure, the road sections with low correlation are mostly high-grade roads such as urban loops, express ways and the like, namely, the road sections bear the task of transferring urban traffic flow with large range and long span, the connection among different blocks of the city is emphasized, the correlation with surrounding neighborhood road sections is weak, and the road sections are blocked, so that the traffic flow of the adjacent road sections cannot be influenced, and the long-distance road sections can be influenced.
In order to further explore the spatial correlation between the road sections with strong heterogeneity of surrounding nodes, the win road with weak local spatial-temporal correlation is extracted as a target road section, and the road section remote correlation mode is analyzed by taking the win road section as an example. Based on
Figure GDA0003408220090000141
Fig. 15 shows a spatial visualization result of the association degree between the wretched road and other road segments, and fig. 15 is a schematic diagram of a spatial remote correlation association mode of a typical road segment according to an embodiment of the present invention; the Jinghan Daodao, youth road and Xinhua road are associated masters embodied by three traffic distribution steady-state valuesThe road is needed. The three roads and the target road section do not have close neighborhood relation in space, but are also used as high-grade express channels in cities, and the related road sections are main driving-in and driving-out sources of the traffic flow of the target road section. Therefore, blocking the target road section has the greatest influence on the flow of road sections such as the Jinghan road, the youth road, the Xinhua road and the like.

Claims (5)

1. A road network traffic flow change quantitative calculation method for road blockade is characterized by comprising the following steps:
s1, a quantitative expression model of a road section node flow dynamic value is provided by analyzing the characteristics of time dependence and flow accumulation of the road network flow;
s2, providing a cascade reaction function of flow rate change between nodes of the origin-destination road section by tracking a micro disturbance diffusion propagation process of the flow of the road network;
s3, providing a traffic flow disturbance response matrix between road sections;
s4, providing a correlation model integrating a time lag operator and a space diffusion operator to evaluate the influence of traffic flow time-space delay on road section correlation;
s5, providing road section dynamic association degree measurement, and calculating the change of road network traffic flow when a road is blocked;
the step S1 is specifically to provide a dynamic traffic system, calculate the nodes of the route sectionsr j Flow disturbance ∂x j Cause toTTime noder i Instantaneous value of flow of
Figure 973199DEST_PATH_IMAGE001
Comprises the following steps:
Figure 854568DEST_PATH_IMAGE002
wherein the content of the first and second substances,x i (t 0) For road section nodesr i In thatt 0A flow value at a time;t 0=0 denotes a noder i The initial flow rate of (a);f(∂x j , t) To be at the moment of timetRoad section noder j Flow disturbance ∂x j To road section noder i Is a mapping relation function of the flow change oftTime road section noder i Instantaneous traffic variation of (2);
the instantaneous traffic variation is determined according to a general equation of a network dynamic process, specifically, a weighted directed network is arrangedA ij ComprisesNNodes, the attribute value of each node being time-dependentx i (t) Expressed, the network dynamics is expressed as the following general equation:
Figure 476042DEST_PATH_IMAGE003
wherein the content of the first and second substances,
Figure 126466DEST_PATH_IMAGE004
representing road segment nodesr i Flow rate value oftA derivative of time;x i (t) For road section nodesr i In thattA flow value at a time;x j (t) For road section nodesr j In thattA flow value at a time;M 0(x i (t) Express link nodex i Automatic state change of (2);
Figure 909614DEST_PATH_IMAGE005
representing captured road segment nodesr j To road section noder i Cascading effects of attribute values;M=(M 0(x), M 1(x), M 2(x) ) is a nonlinear system of equations describing the dynamic process of a complex network system;M 1(x i (t) Is a road segment noder i Increment of flow rate value of Δ: (x i (t))；M 2(x j (t) Is a road segment noder j Inverse 1/Δ of flow value increment (1/Δ:)x j (t))；
The step S2 is specifically to set the current traffic flow to pass through the link nodes in sequence from the O-linkr j Road section noder k And road section noder i Reach D road section, and at the same time, reach the road section noder j Connecting road section nodes via a first blank noder i Node of road sectionr k Connecting the D road section through a second blank node; road section noder j Subject to a disturbance of flow of∂x j ，tTime later road section noder i For traffic flowf(∂x j , t) Represents; according to the general equation of network dynamic processf(∂x j , t) Is interfered by two parts of flow, including traffic outflow and traffic inflow;
the traffic outflow is specifically 0-tNode of slave road section in timer i Outgoing traffic volume; the traffic inflow is specifically 0-tNode of road section within timer j Flow direction road section noder i The amount of traffic of (2); road section noder j And road section noder i A plurality of flow channels exist between the road sections, and the road sections are connected by the nodes because the traffic flow is only influenced by the upstreamr i Traffic flow off(∂x j , t) Comprises the following steps:
Figure 543858DEST_PATH_IMAGE006
wherein 0-tIn the time of day, the user can select the time,∂x i (∂x j (t) Is a road segment noder j Disturbed flow∂x j In the case of (2), link noder i Traffic outflow variation of (1);∂x k (∂x j (t) Is a road segment noder k Road junctionr j Traffic inflow variation amount of (a);N i for road section nodesr i The number of upstream adjacent road segment nodes.
2. The road network traffic flow change quantitative calculation method oriented to road blockade according to claim 1, wherein the step S3 is a traffic flow disturbance response matrixG ij Is defined as follows:
Figure 437865DEST_PATH_IMAGE007
wherein the content of the first and second substances,Tis the time;N ij for nodes of slave linksr j Road to section noder i Road segment node contained in feasible pathr i The number of upstream contiguous segments of (a);krepresenting road segment nodesr k ID identification of (a);R ik (T) For road sectionsr i In thatTTime-to-road segment noder k Local response to traffic flow disturbances;G kj (T) For road section nodesr k In thatTTime-to-road segment noder j Response to traffic flow disturbances.
3. The road network traffic flow change quantitative calculation method oriented to road blockade as claimed in claim 2, wherein the traffic flow disturbance response matrixG ij The calculation method is specifically that of the road sectionr k At the moment of timeTTo road section noder j Local response to traffic flow disturbancesR kj (T) Watch (A)The method comprises the following steps:
Figure 473954DEST_PATH_IMAGE009
wherein the content of the first and second substances,Tis the time;x k (T) For road section nodesr k In thatTA traffic flow response value at a moment;x j (T) For road section nodesr j In thatTA traffic flow response value at a moment;
traffic flow disturbance response matrixG ij The calculation method comprises the following steps of tracking the source road section noder i Local disturbance to the traffic flow at a certain time affects the traffic flow activity of all the nodes of the remaining road segments in the traffic system to track the propagation of traffic signals in the dynamic traffic system proposed in step S1, thereby generating a traffic flow response matrix; and according to the link node in step S2r i Traffic flow off(∂x j , t) Node of road sectionr i At the moment of timeTTo road section noder j Response expression of traffic flow disturbanceG ij (T) Comprises the following steps:
Figure 428003DEST_PATH_IMAGE010
wherein the content of the first and second substances,dx j (t) For road section nodesr j In thattA traffic change amount at a time;krepresenting road segment nodesr k ID identification of (a);N ij for nodes of slave linksr j Road to section noder i Road segment node contained in feasible pathr i The number of upstream contiguous segments of (a);∂x i (∂x j (t) Is a road segment noder j Disturbed flow∂x j In the case of (2), link noder i Traffic outflow variation of (1);∂x k (∂x j (t) Is a road segment noder k Road junctionr j Traffic inflow variation amount of (a);x i (T) For road section nodesr i In thatTA traffic flow response value at a moment;x j (T) For road section nodesr j In thatTA traffic flow response value at a moment;
Figure 283964DEST_PATH_IMAGE011
representing an automatic flow rate of change;
Figure 388186DEST_PATH_IMAGE012
representing road sectionsr j For road sectionr i The effect of the rate of change of flow;
the diffusion propagation of the traffic in the road network from the O-segments to the D-segments will create a cascading effect of the traffic attribute values,r j the flow of the node firstly influences the local neighborhood road sectionr k Flow rate value of (2) further tor i The road segment is propagated, and therefore,r j to pairr i Into a flow steady state valuer j →r k →r i Chain reactions, particularly joining road sectionsr i At the moment of timeTTo road section noder j Response expression of traffic flow disturbanceG ij (T) Expressed as:
Figure 872257DEST_PATH_IMAGE013
wherein the content of the first and second substances,dx j (T) Is composed ofTTime road section noder j The difference between the steady state attribute value of (a) and the initial value;dx k (T) For road section nodesr k In thatTA traffic change amount at a time;N ij for nodes of slave linksr j Road to section noder i Road segment node contained in feasible pathr i The number of upstream contiguous segments of (a);x i (T) For road section nodesr i In thatTA traffic flow response value at a moment;x j (T) For road section nodesr j In thatTA traffic flow response value at a moment;jrepresenting road segment nodesr j ID identification of (a);
Figure 138153DEST_PATH_IMAGE014
is the automatic flow rate of change;
Figure 606044DEST_PATH_IMAGE015
is the cascade effect of the road section flow rate change.
4. The road network traffic flow change quantitative calculation method oriented to road blockade according to claim 3, wherein the step S4 is to set a time lag operator ass；xAndytraffic flow response matrix for two road segmentsG ij Samples are delayed in timesLower road sectionxAndycorrelation between themρ(s) Can be expressed as:
Figure 248378DEST_PATH_IMAGE016
wherein the content of the first and second substances,μ x for road section nodesxAverage value of traffic flow;μ y for road section nodesyAverage value of traffic flow; cov (,) is covariance;E(. to) is a mathematical expectation;
Figure 993480DEST_PATH_IMAGE017
for road section nodesxVariance of traffic flow;
Figure 554911DEST_PATH_IMAGE018
for road section nodesyVariance of traffic flow;y(t+s) Is composed ofyThe road section ist+sA traffic flow response value at a moment;x(t) Is composed ofxThe road section istA traffic flow response value at a moment;
however, road section nodes in urban traffic networks are usually related to a plurality of neighborhood road sections, and in order to characterize the spatial correlation range of the local correlation of the road sections, a spatial diffusion operator is used for representingkTo describekWeighted average response value of order neighborhood section
Figure 385464DEST_PATH_IMAGE019
：
Figure 693473DEST_PATH_IMAGE020
Wherein the content of the first and second substances,
Figure 27502DEST_PATH_IMAGE021
representing road segment nodesr a Flow response generated by disturbance flows of all road sections in a road network;Nthe number of road sections in a road network;ω ij for road section nodesr i And road section noder j According to the neighborhood parameterskDetermining;mkfor nodes of road sections in road networkr i Adjacent to each otherkThe number of order neighborhood segments;bfor road section nodesr b ID identification of (a);jfor road section nodesr j ID identification of (a);
integration time lag operatorsAnd spatial diffusion operatorkInfluence on local correlation of road network, and road sections and road segments in traffic road network by describing time delaykReflecting traffic flow response associations between adjacent road segmentsLocal spatio-temporal heterogeneity ofρ i (k,s) The concrete expression is as follows:
Figure 900781DEST_PATH_IMAGE022
wherein the content of the first and second substances,Tis the time;x i (t) For road section nodesr i In thattA traffic flow response value at a moment;
Figure 77684DEST_PATH_IMAGE023
for road section nodesr i Is/are as followskOrder neighborhood road section traffic flow response value ist+sA weighted average of the time;
Figure 61821DEST_PATH_IMAGE024
for road section nodesr i In thattAverage value of traffic flow response value at the moment;
Figure 640569DEST_PATH_IMAGE025
for road section nodesr i Is/are as followskAverage value of traffic flow response values of the order neighborhood road sections.
5. The road network traffic flow change quantitative calculation method oriented to road blockade according to claim 4, wherein the step S5 is specifically based on the traffic flow disturbance response matrix in the step S3G ij Road network global traffic flow to road section noder i Dynamic disturbance response ofR i (T) The quantization is as follows:
Figure 684749DEST_PATH_IMAGE027
wherein the content of the first and second substances,j≠i；jrepresenting road segment nodesr j ID identification of (a);irepresenting road segment nodesr i ID identification of (a);Nthe number of all nodes in the road network;G ij (T) For road section nodesr i At the moment of timeTTo road section noder j Response expression of traffic flow disturbance;
introducing link variation set parametersπ={π a ,π b And (c) the step of (c) in which,
Figure 224315DEST_PATH_IMAGE028
a failure set of road segments is represented, wherein,r l the number of the failed road segment is the number,r l rate of decline of capacity for road sectionr l ∈[0,1]，naThe number of the failed road sections;
Figure 871197DEST_PATH_IMAGE029
in order to add a new set of road segments,q f numbering newly added road sections;o f is the starting point ID of the initial path;d f is the end point ID of the initial road segment,nbthe number of newly added road sections;
road sectionr i Degree of dynamic association with other road segments
Figure 445397DEST_PATH_IMAGE030
Comprises the following steps:
Figure 519533DEST_PATH_IMAGE032
wherein the content of the first and second substances,j≠i；jrepresenting road segment nodesr j ID identification of (a);irepresenting road segment nodesr i ID identification of (a);
Figure 546394DEST_PATH_IMAGE033
for road sectionsr i Responding to dynamic disturbance of the road network global traffic flow;
Figure 872334DEST_PATH_IMAGE034
for road network global traffic flow to road section node after introducing road section variation set parameterr i The dynamic disturbance response of (2);Nthe number of all nodes in the road network;
Figure 425675DEST_PATH_IMAGE035
for road section nodesr i At the moment of timeTTo road section noder j Response expression of traffic flow disturbance;
Figure 546077DEST_PATH_IMAGE036
for road section aggregationπAnd changing and redistributing the traffic flow disturbance response matrix.
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Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101067902A (en) * 2006-11-30 2007-11-07 上海交通大学 Traffic flow state analysis required detection vehicle sampling quantity obtaining method
CN102314768A (en) * 2010-06-30 2012-01-11 西门子公司 Traffic information collecting method, device and system
CN106448151A (en) * 2016-07-07 2017-02-22 河南理工大学 Short-time traffic flow prediction method
CN109285346A (en) * 2018-09-07 2019-01-29 北京航空航天大学 A kind of city road net traffic state prediction technique based on key road segment
CN109887282A (en) * 2019-03-05 2019-06-14 中南大学 A kind of road network traffic flow prediction technique based on level timing diagram convolutional network
CN112581757A (en) * 2020-12-09 2021-03-30 曾李 Intelligent traffic information processing method and device and server

Family Cites Families (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105531746B (en) * 2013-08-08 2018-01-09 意大利电信股份公司 The management for the data collected for flow analysis
GB2518662B (en) * 2013-09-27 2015-12-16 Thales Holdings Uk Plc Apparatus and method for managing traffic
US10074272B2 (en) * 2015-12-28 2018-09-11 Here Global B.V. Method, apparatus and computer program product for traffic lane and signal control identification and traffic flow management
CN109961180A (en) * 2019-03-15 2019-07-02 浙江工业大学 A kind of short-term traffic flow forecast method based on temporal correlation
CN110047291B (en) * 2019-05-27 2020-06-19 清华大学深圳研究生院 Short-term traffic flow prediction method considering diffusion process
CN111383452A (en) * 2019-12-03 2020-07-07 东南大学 Method for estimating and predicting short-term traffic running state of urban road network
CN112466117A (en) * 2020-11-24 2021-03-09 南通大学 Road network short-term traffic flow prediction method based on deep space-time residual error network
CN112185124B (en) * 2020-11-27 2021-03-26 深圳市城市交通规划设计研究中心股份有限公司 Method and device for predicting traffic state of whole road network

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101067902A (en) * 2006-11-30 2007-11-07 上海交通大学 Traffic flow state analysis required detection vehicle sampling quantity obtaining method
CN102314768A (en) * 2010-06-30 2012-01-11 西门子公司 Traffic information collecting method, device and system
CN106448151A (en) * 2016-07-07 2017-02-22 河南理工大学 Short-time traffic flow prediction method
CN109285346A (en) * 2018-09-07 2019-01-29 北京航空航天大学 A kind of city road net traffic state prediction technique based on key road segment
CN109887282A (en) * 2019-03-05 2019-06-14 中南大学 A kind of road network traffic flow prediction technique based on level timing diagram convolutional network
CN112581757A (en) * 2020-12-09 2021-03-30 曾李 Intelligent traffic information processing method and device and server

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